Digital Tools and Machine Learning for early Leak Detection in Hydrogen Pipeline

The goal of the proposed novel digitalized system is to take preventive action by artificial intelligence without requiring human interference. The analysis will be based on laboratory set up (pipeline loop) of single phase (air/H2 or H2/CH4 mix), and two phase (gas/water) mixture and its relation to the leak identification and dispersion prediction. In this project for leak characterization, we will use high-speed visualization, pressure sensors, temperature sensors, differential pressure, dynamic pressure sensor, and flowmeter as the internal leak detection methods. We will also deploy several external leak detection methods such as laser diagnostics, high-speed visualization, fiber optic cables, and thermal camera.

To support local SME ’M-connected LTD’ for expanding their business into application of machine learning for gas leak detection during H2 transportation.